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Install in OpenClaw
/install ai-agent-helper-moss
Description
AI Agent 設定同優化助手 - Prompt Engineering、Task Decomposition、Agent Loop設計
README (SKILL.md)
AI Agent Helper
幫你setup同優化AI Agents既技能。
功能
- 📝 Prompt Engineering - 整高質量system prompts
- 🔄 Task Decomposition - 將複雜任務拆解
- ⚙️ Agent Loop設計 - ReAct/ReAct/Chain-of-Thought
- 🎯 Tool Selection - 最佳化agent既tool usage
使用場景
"帮我整prompt" / "點樣set AI agent" / "優化agent response"
技術
- System Prompt優化
- Few-shot examples
- Output parsing (JSON/structured)
- Error handling patterns
- Token優化
範例
# Good prompt structure
system = """你係{role}。
目標：{goal}
限制：{constraints}
Output格式：{format}"""
Usage Guidance
This is an instruction-only skill for prompt engineering and agent-loop design and does not request secrets or install code, so it's internally coherent and low-risk. Before installing, consider: 1) the skill's source and author are unknown—prefer skills with a verifiable homepage or trusted publisher; 2) the _meta.json metadata differs from the registry metadata (ownerId/slug/version mismatch), which is likely a packaging mistake but worth noting; 3) never paste sensitive credentials or private data into prompts or examples provided to the skill; and 4) if you need stronger assurance, ask the publisher for a canonical source or a signed release.
Capability Analysis
Type: OpenClaw Skill
Name: ai-agent-helper-moss
Version: 1.0.1
The skill bundle is a purely informational guide for prompt engineering and AI agent optimization. It contains no executable code, network requests, or suspicious instructions, focusing entirely on providing templates and best practices for structuring AI prompts and workflows within SKILL.md.
Capability Assessment
Purpose & Capability
The skill's name, description, and runtime instructions all describe prompt engineering and agent-loop design; no extra capabilities or credentials are requested. Minor packaging inconsistencies exist (different ownerId/slug/version in _meta.json vs registry metadata), which look like a metadata error rather than malicious behavior.
Instruction Scope
SKILL.md contains bounded guidance for prompt structure, decomposition, and agent loop patterns. It does not instruct the agent to read files, access environment variables, contact external endpoints, or exfiltrate data.
Install Mechanism
No install spec and no code files — instruction-only. This minimizes disk/network risk.
Credentials
No environment variables, credentials, or config paths are requested. This is proportional to a prompt-engineering helper.
Persistence & Privilege
No 'always: true' or other elevated persistence requested; default agent invocation settings are used.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install ai-agent-helper-moss - After installation, invoke the skill by name or use
/ai-agent-helper-moss - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.1
- Updated the skill name from "ai-agent-helper-moss" to "ai-agent-helper-moss-moss" in SKILL.md.
- No other changes to features or content.
v1.0.0
- Initial release of AI Agent Helper (版本 1.0.1)
- 提供 Prompt Engineering、Task Decomposition、Agent Loop 設計等功能
- 幫助 setup 及優化 AI Agents，包括 Tool Selection
- 支援技術如 system prompt 優化、few-shot examples、output parsing、error handling、token優化
- 適用於構建和優化 AI agent prompts 及回應
Metadata
Frequently Asked Questions
What is Ai Agent Helper Moss?
AI Agent 設定同優化助手 - Prompt Engineering、Task Decomposition、Agent Loop設計. It is an AI Agent Skill for Claude Code / OpenClaw, with 779 downloads so far.
How do I install Ai Agent Helper Moss?
Run "/install ai-agent-helper-moss" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Ai Agent Helper Moss free?
Yes, Ai Agent Helper Moss is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Ai Agent Helper Moss support?
Ai Agent Helper Moss is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Ai Agent Helper Moss?
It is built and maintained by MemoryF (@memoryf); the current version is v1.0.1.
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